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## Model Description
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Based on [this paper](https://
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*Note: We generally recommend choosing the [EnvironmentalBERT-base](https://huggingface.co/ESGBERT/EnvironmentalBERT-base) model since it is quicker, less resource-intensive and only marginally worse in performance.*
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## More details can be found in the paper
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```bibtex
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@article{
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}
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```
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## Model Description
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Based on [this paper](https://www.sciencedirect.com/science/article/pii/S1544612324000096), this is the EnvRoBERTa-base language model. A language model that is trained to better understand environmental texts in the ESG domain.
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*Note: We generally recommend choosing the [EnvironmentalBERT-base](https://huggingface.co/ESGBERT/EnvironmentalBERT-base) model since it is quicker, less resource-intensive and only marginally worse in performance.*
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## More details can be found in the paper
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```bibtex
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@article{schimanski_ESGBERT_2024,
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title = {Bridging the gap in ESG measurement: Using NLP to quantify environmental, social, and governance communication},
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journal = {Finance Research Letters},
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volume = {61},
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pages = {104979},
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year = {2024},
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issn = {1544-6123},
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doi = {https://doi.org/10.1016/j.frl.2024.104979},
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url = {https://www.sciencedirect.com/science/article/pii/S1544612324000096},
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author = {Tobias Schimanski and Andrin Reding and Nico Reding and Julia Bingler and Mathias Kraus and Markus Leippold},
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}
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```
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